Automated Author ProfileDececchi, Thomas Alexander
Dececchi, Thomas Alexander
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 7.5 (sum of 15 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
No description available
Authors
- Dececchi, Thomas Alexander ;
- Narbonne, Guy M. ;
- Greentree, Carolyn ;
- Laflamme, Marc
No description available
Authors
- Dececchi, Thomas Alexander ;
- Narbonne, Guy M. ;
- Greentree, Carolyn ;
- Laflamme, Marc
No description available
Authors
- Dececchi, Thomas Alexander ;
- Narbonne, Guy M. ;
- Greentree, Carolyn ;
- Laflamme, Marc
The reality of larger and larger molecular databases and the need to integrate data scalably have presented a major challenge for the use of phenotypic data. Morphology is currently primarily described in discrete publications, entrenched in noncomputer readable text, and requires enormous investments of time and resources to integrate across large numbers of taxa and studies. Here we present a new methodology, using ontology-based reasoning systems working with the Phenoscape Knowledgebase (KB; kb.phenoscape.org), to automatically integrate large amounts of evolutionary character state descriptions into a synthetic character matrix of neomorphic (presence/absence) data. Using the KB, which includes more than 55 studies of sarcopterygian taxa, we generated a synthetic supermatrix of 639 variable characters scored for 1051 taxa, resulting in over 145,000 populated cells. Of these characters, over 76% were made variable through the addition of inferred presence/absence states derived by machine reasoning over the formal semantics of the source ontologies. Inferred data reduced the missing data in the variable character-subset from 98.5% to 78.2%. Machine reasoning also enables the isolation of conflicts in the data, that is, cells where both presence and absence are indicated; reports regarding conflicting data provenance can be generated automatically. Further, reasoning enables quantification and new visualizations of the data, here for example, allowing identification of character space that has been undersampled across the fin-to-limb transition. The approach and methods demonstrated here to compute synthetic presence/absence supermatrices are applicable to any taxonomic and phenotypic slice across the tree of life, providing the data are semantically annotated. Because such data can also be linked to model organism genetics through computational scoring of phenotypic similarity, they open a rich set of future research questions into phenotype-to-genome relationships.
Authors
- Dececchi, Thomas Alexander ;
- Balhoff, James P. ;
- Lapp, Hilmar ;
- Mabee, Paula M.
No description available
Authors
- Dececchi, Thomas Alexander ;
- Balhoff, James P. ;
- Lapp, Hilmar ;
- Mabee, Paula M.
No description available
Authors
- Dececchi, Thomas Alexander ;
- Balhoff, James P. ;
- Lapp, Hilmar ;
- Mabee, Paula M.
No description available
Authors
- Dececchi, Thomas Alexander ;
- Balhoff, James P. ;
- Lapp, Hilmar ;
- Mabee, Paula M.
No description available
Authors
- Dececchi, Thomas Alexander ;
- Balhoff, James P. ;
- Lapp, Hilmar ;
- Mabee, Paula M.
No description available
Authors
- Dececchi, Thomas Alexander ;
- Balhoff, James P. ;
- Lapp, Hilmar ;
- Mabee, Paula M.